Querying in the Age of Graph Databases and Knowledge Graphs
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Abstract (arXiv)
Graphs have become the best way we know of representing knowledge. The computing community has investigated and developed the support for managing graphs by means of digital technology. Graph databases and knowledge graphs surface as the most successful solutions to this program. The goal of this document is to provide a conceptual map of the data management tasks underlying these developments, paying particular attention to data models and query languages for graphs.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 82 references that are in the map. First posted on arXiv 22 Jun 2021.
Builds on
12 of 82 references.
References · 12
- 2018How Powerful are Graph Neural Networks?cited by 4,393
- 2020Knowledge Graphscited by 621
- 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
- 2020Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddingscited by 171
- 2018Embedding Logical Queries on Knowledge Graphscited by 120
- 2021The Logic of Graph Neural Networkscited by 85
- 2020Model Interpretability through the Lens of Computational Complexitycited by 75
- 2016Foundations of Modern Query Languages for Graph Databasescited by 22
- 2020Local Graph Clustering with Network Lassocited by 16
- 2017G-CORE: A Core for Future Graph Query Languagescited by 13
- 2019Near Neighbor: Who is the Fairest of Them All?cited by 7
- 2019Fair Near Neighbor Search: Independent Range Sampling in High Dimensionscited by 5
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
2 papers.